Emissions Tracking

What is "Emissions Tracking"?

Emissions tracking involves accurately tracking and predicting
emissions from various sources and is important for several reasons. Obviously
there is the regulatory aspect; exceed limits and you can
get fined or even have to shut down a process. But there
are other important reasons to know what’s going on with
your emissions. With “greenhouse gasses” being a hot
topic these days, one use of the DataPipe modules is
calculating "greenhouse
gas emission".

If you keep track over time you can see how
the processes are behaving. Being able to estimate when,
say, a monthly cap on cumulative emissions may be reached
can help you schedule processes. Isn’t it better to know
in advance than find out after the fact? If emissions are
going to be under limits you may be able to sell or trade
“credits.”

How does DataPipe deal with Emissions
tracking?

DataPipe has several ways for tracking
emissions, one based on current measurements the other on
calculations. Either or both may be used individually or
in combination, based on your particular (and possibly
changing) needs.

The Source Tests Module tracks “sources,”
chemical and physical limits (regulatory and/or
self-imposed), and the results of tests on each “source”
over time.

A “source” may be anything you want.
Obvious candidates are stacks and outfalls; there are
others (valves, vehicles, etc.) Sources may be air,
water, waste and anything else your heart desires.
Sources can have chemical and/or physical limits, with
limits being below or above values, in or out of a range,
etc. However, a limit need not be set for a test to be
conducted.

Source tests track the results of periodic
testing of the source. Again, chemical and/or physical
parameters may be included in a test or tests. A chemical
test would be something like how much of various agents
(agents are referenced to the Agent Parameters database in
DataPipe) are present. In a stack this might be SO2, NOx,
solvents, etc. Physical tests might be for pH,
temperature, opacity, etc.

How are results entered in DataPipe?

As with any DataPipe module, results can be
entered multiple ways. You can always hand enter the
results of a test (if you have the necessary security
permissions on the form). But could also set up a process
to import results e-mailed in from a lab and/or take a
feed from a continuous emissions monitoring system (CEM).

If you do have limits established for any
sources, as you enter test results they are automatically
compared to the limits and you get visible feedback.
Later, as you step through test records the comparisons
are made, so you always can see how a set of tests
compared to the limits.

As previously mentioned, a “source” can be
man things and need not be the typical exhaust stack or
discharge pipe. In one instance a utility customer (who
you would expect to have the typical sources for air and
water emissions) also used the Source Test Module for
tracking water fountains, of which they had over 300.
Each fountain was set up as a “source.” Monthly tests
were collected from each fountain and the results entered
in DataPipe. They were interested in both chemical
properties (mostly various metals, like lead and copper)
and physical properties such as pH, temperature, turbidity
(cloudiness) and suspended solids.

As we said, almost anything can be set up
as a “source” for testing!

The second type of emissions information is
based on calculations and using “emissions
factors” along with other information. If you’re
familiar with AP-42, the Environmental Protection Agency’s
(EPA) table of emission factors and how they’re used you
have some idea of how the DataPipe Process Model Module
works.

An emission factor is a rare number or
formula used to calculate the quantity of an emission
produced based on some variable in the operating or
manufacturing process. Here’s what the EPA says:

Emissions Factors & AP 42

An
emissions factor is a representative value
that attempts to relate the quantity of a pollutant
released to the atmosphere with an activity associated
with the release of that pollutant. These factors are
usually expressed as the weight of pollutant divided
by a unit weight, volume, distance, or duration of the
activity emitting the pollutant (e.g., kilograms of
particulate emitted per megagram of coal burned). Such
factors facilitate estimation of emissions from
various sources of air pollution. In most cases, these
factors are simply averages of all available data of
acceptable quality, and are generally assumed to be
representative of long-term averages for all
facilities in the source category (i.e., a population
average).

The general
equation for emissions estimation is:

E = A x EF x (1-ER/100)

where:

E
= emissions;

A
= activity rate;

EF
= emission factor, and

ER
=overall emission reduction efficiency, %

For example, if you’re manufacturing
batches of polystyrene here are AP-42 emissions for
various steps in the process:

c.
Reference 4. The higher factors are more likely during
the manufacture of lower molecular weight products.
Factor for any given process train will change with
product grade.

The major
vent is the devolatilizer condenser vent (Stream C). This
continuous offgas vent emits 0.25 to 0.75 grams of VOC per
kilogram (gVOC/kg) of product depending on the molecular

weight of the polystyrene product being
produced. If we split the difference to formula for the
production of nonmethane VOC would be

0.5g/Kg*Kg Product Produced

Other parts of the process would have their
equations, all based on Kg of product produced. We could
sum up everything into one equation or keep each part
separate, as needed.

Process model calculations in DataPipe can
be much more complex. Multiple independent variables can
be defined for a source, them multiple formulas, one for
each output can be set up. DataPipe has its own
“calculator” so as the quantities of the inputs are
entered the formulas are evaluated to determine how much
of something is created or released over the time
interval.

For example, calculating the amount of
Toluene vapor lost when a storage tank is filled (the
“working loss) depends on five (5) independent variables:

X01 - Liquid pumped to the tank over
time, ft^3 (Qw)

X02 - Ambient temperature, F (Temp)

X03 - Day-night temperature fluctuation
(Delta Temp)

X04 - Liquid level in tank, as % of
height (L-Level)

X05 - Vapor pressure at ambient temp,
mmHg (VP)

Using a conventional formula for
calculating the pounds of Toluene lost, the formula in
DataPipe is:

x01*(1/359)*(273.15/((5/9)*(x02-32)+273.15))*(x05/760)*1*1

Given the five input variables DataPipe can
calculate the greenhouse gas emissions from the tank
filling process.

Here is the information for an older
diesel-powered heavy truck. The inoput variables are

X01 - Cumulative Mileage (odometer
reading)

X02 - Miles driven for the month (change
in miles)

For CO emissions, in grams:

(11.440 +
(0.160 * (X01/10000))) * X02

For hydrocarbon emissions, in grams:

(3.920 +
(0.060 * (X01/10000))) * X02

And for NOx emissions, in grams:

(25.500 +
(0.190 * (X01/10000))) * X02

In this example entering the odometer
reading and the miles driven for the month one time all
the emissions for the vehicle can be calculated. You
could set up appropriate formulas for each vehicle, feed
in the mileage information from your fleet information
system (or the DataPipe MV Fleet Module) and do all the
calculations for the fleet for the month.

In one instance a customer had to calculate
the monthly fugitive dust emissions from an open pit
mine. One source was the trips taken by truck, which were
in three sizes. They determined how much dust (pounds) of
dist was generated for each truck type on a round trip.
They set up a formula using three independent variables,
the number of trips per month of each of the three truck
type. The formula used the amount of duct generated by
each truck type times the number of trips for that truck
type and added the results for the total dust generated
for the month. Each month they just enter the number of
truck trips for each of the three types and the
calculation is made.

You may
determine emission factors based on anything you want;
hours of operation, number of widgets produced, amount of
fuel consumed, quantity and type of appliances spray
painted, etc. The actual testing of your sources can be
used as a basis for the emission factors if you don’t use
AP-42 or similar published factors (they can’t determine
factors for everything!). Your formulas for calculating
the emissions of as many things as you want are then based
on these input parameters.

DataPipe is a complete EH&S information
management software solution. If you are interested
in taking a look at our modules or want to learn more
about
occupational health software and the various modules
within that suite or if you want to look at
industrial
hygiene software and the various modules that make up
that suite, please review our
modules page.

DataPipe USA Inc. (KAI) is the leading US and Global
provider of Environmental, Health & Safety (EHS) and
Crisis Management software. The company, headquartered
in Butler, new jersey was founded in 1979 as the first
EHS Software developer on the scene, integrating EHS
instruments long before PCs were on the market. It
wasn't until 1989 when the very fist windows based EHS
software application - DataPipe - was released (and it
really was one of the first windows based software
applications - period). Since then KAI has grown to be
the premier EHS software provider with a global client
base representing companies of all sizes from many major
industry sectors.